Nonparametric Tree Graphical Models via Kernel Embeddings

By Le Song, Arthur Gretton, Carlos Guestrin. 2010

17 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “Song et al. (2010a) relies on the embeddings of the distributions over observations and latent states, and then constructs an operator that represents the joint probabilities in the feature space.”
Kernel Mean Shrinkage Estimators (2014, methodology): “…appealing to many algorithms in modern kernel methods, namely, two-sample testing via maximum mean discrepancy (MMD) (Gretton et al., 2007, 2012), kernel independence tests (Gretton et al., 2008), Hilbert space embedding of HMMs (Song et al., 2010), and kernel Bayes rule (Fukumizu et al., 2011).”
Hilbert Space Embeddings of POMDPs (2012, methodology): “Recently, inference methods which make use of the embeddings have been proposed, including for Hidden Markov Models [Song et al., 2009, Song et al., 2010], and belief propagation [Song et al., 2010, Song et al., 2011].”
Kernel Bayes' Rule (2010, methodology): “More recently, a method for belief propagat ion using kernel means was proposed [18, 19]: unlike the present work, this directly estimates c onditional densities assuming the prior to be uniform.”
Path Integral Control by Reproducing Kernel Hilbert Space Embedding (2012, unknown): “Lemma 3 (Song et al., 2010) .”
Kernel Bayes' rule: Bayesian inference with positive definite kernels (2013, methodology): “Most relevant to this paper are Song et al. (2009) and Song et al. (2010b), in which the kernel means and covariance operators are used to implement a nonparametric HMM.”